Ultrasonic scanners for detecting blood flow based on the Doppler effect are well known. Such systems operate by actuating an ultrasonic transducer array to transmit ultrasonic waves into the object and receiving ultrasonic echoes backscattered from the object. For blood flow measurements, returning ultrasonic waves are compared to a frequency reference to determine the frequency shifts imparted to the returning waves by moving objects including the vessel walls and the red blood cells inside the vessel. These frequency shifts translate into velocities of motion. An intensity-modulated Doppler frequency versus time spectogram is displayed since the Doppler sample volume or range cell generally contains a distribution of velocities that can vary with time.
In state-of-the-art ultrasonic scanners, the pulsed or continuous wave Doppler waveform is computed and displayed in real-time as a gray-scale spectrogram of velocity versus time with the gray-scale intensity (or color) modulated by the spectral power. The data for each spectral line comprises a multiplicity of frequency data bins for different frequency intervals, the spectral power data in each bin for a respective spectral line being displayed in a respective pixel of a respective column of pixels on the display monitor. Each spectral line represents an instantaneous measurement of blood flow.
In the conventional spectral Doppler mode, an ultrasound transducer array is activated to transmit by a transmit ultrasound burst which is fired repeatedly at a pulse repetition frequency (PRF). The PRF is typically in the kilohertz range. The return radiofrequency (RF) signals are detected by the transducer elements and then formed into a receive beam by a beamformer. For a digital system, the summed RF signal from each firing is demodulated by a demodulator into its in-phase and quadrature (I/Q) components. The I/Q components are integrated (summed) over a specific time interval and then sampled. The summing interval and transmit burst length together define the length of the sample volume as specified by the user. This so-called "sum and dump" operation effectively yields the Doppler signal backscattered from the sample volume. The Doppler signal is passed through a wall filter, which is a high pass filter that rejects any clutter in the signal corresponding to stationary or very slow-moving tissue, including a portion of the vessel wall(s) that might be lying within the sample volume. The filtered output is then fed into a spectrum analyzer, which typically takes Fast Fourier Transforms (FFTS) over a moving time window of 64 to 256 samples. The FFT output contains all the information needed to create the video spectral display as well as the audio output (typical diagnostic Doppler ultrasound frequencies are in the audible range).
For video display, the power spectrum is computed by taking the power or absolute value squared, of the FFT output. The power spectrum is compressed and then displayed via a gray-scale mapping on the monitor as a single spectral line at a particular time point in the Doppler velocity (frequency) versus time spectrogram. The positive frequency [0:PRF/2] spectrum represents flow velocities towards the transducer, whereas the negative frequency [-PRF/2:0] spectrum represents flow away from the transducer. An automatic Doppler maximum/mean waveform tracing is usually performed after the FFT power spectrum has been compressed. The computed maximum/mean velocity traces are usually presented as overlay information on the spectrogram display. More importantly, the values of the maximum frequency (f.sub.max) trace or "envelope" of the Doppler spectrogram at different points in the cardiac cycle is used in a number of diagnostic indices. In fact, it has been reported that an abnormally high f.sub.max or v.sub.max at peak systole alone is a good indicator of vascular stenosis. Also, v.sub.max is used to estimate the pressure drop across a stenosis based on the Bernoulli equation.
Whereas the mean frequency or velocity is defined by the first moment of the Doppler spectrum, the maximum frequency can be challenging to detect in a consistent manner, especially under weak SNR conditions. In particular, the maximum frequency waveform tracing can be based on the positive frequency spectrum only, the negative frequency spectrum only, or the composite spectrum for which the highest absolute frequency value at each time point is traced.
In an article entitled "Objective algorithm for maximum frequency estimation in Doppler spectral analysers", Med. Biol. Engng. and Comput., Vol. 23, pp. 63-68 (1985), D'Alessio proposed a method of estimating a maximum frequency waveform based on a threshold-crossing technique that takes into account the exponential statistics of the FFT power spectrum of white noise prior to any compression or nonlinear mapping. A modified threshold method and other new methods have also been proposed (see, e.g., Mo et al., "Comparison of four digital maximum frequency estimators for Doppler ultrasound," Ultrasound in Med. & Biol., Vol. 14, pp. 355-363, 1988 and Vaitkus et al., "Development of methods to analyse transcranial Doppler ultrasound signals recorded in microgravity", Med. Biol. Engng. and Comput., Vol. 28, pp. 306-311, 1990), but they are still based on the power spectrum before compression. For realization on a real-time clinical scanner, it is important to trace the mean/maximum frequencies of the compressed spectrum as expressed in gray scale units exactly as they are displayed on the monitor. Unfortunately, the compression (e.g., logarithmic) can substantially alter the statistical distribution of the noise spectral power such that the aforementioned methods are no longer applicable.
An automated method based on the video spectral data is disclosed in U.S. Pat. No. 5,287,753 to Routh et al. The method consists of finding the highest frequency with an intensity equal to a threshold value T, defined as a constant k times an average signal intensity divided by an average noise intensity. The threshold T is updated once every cardiac cycle in order to follow the signal level changes due to changes in instrument setting or movement of the transducer.
An analytic method of tracing the maximum Doppler frequency waveform is taught in U.S. patent application Ser. No. 08/944,119 entitled "Method and Apparatus for Automatic Tracing of Doppler Time-Velocity Waveform Envelope." At the core of this algorithm is a maximum frequency detection mechanism which is based on searching for the highest frequency bin whose spectral amplitude exceeds a certain noise threshold. Unlike the existing methods which require use of one or more empirical constants in setting the threshold level, the method is based on a theoretical noise amplitude distribution in the video spectral domain. In particular, the method of the invention uses a precise model of the statistical distribution of the video spectral power of white noise to establish a threshold for maximum frequency detection. Input to the noise model is the average white noise level in the video spectral display, which can be computed using either of two analytical methods. The predicted threshold versus mean noise level is a highly nonlinear curve, which is key to achieving a robust performance over different display dynamic range settings and SNR conditions
For the audio Doppler output, the positive and negative frequency portions, or sidebands, of the FFT output are split into two separate channels representing the forward and reverse flow spectra respectively. For each channel, the sideband is reflected about the zero frequency axis to obtain a symmetric spectrum, which generates, after an inverse FFT (IFFT) operation, a real-valued flow signal in the time domain. Both the forward and reverse flow signals are converted into analog waveforms, which are fed to the corresponding audio speakers.
In a conventional spectral Doppler system, if the Doppler signal is weak, the Doppler signal gain needs to be increased via manual gain control and/or some built-in automatic gain control, in order to visualize and hear the flow signals clearly. Unfortunately, increasing the Doppler gains also tends to boost the background system noise. Since the system noise usually has a flat power spectral density over the frequency range -PRF/2 to +PRF/2 (excluding the wall filter rejection band), boosting its amplitude can generate a distracting "popping" sound in the audio output.
To reduce the background system noise (i.e., popping sound), simple low-pass filters can be applied to the audio Doppler data before or after digital-to-analog conversion. However, such low-pass filters may also remove important high-frequency flow components which, if present, can extend all the way up to .+-.PRF/2. This problem is complicated by the fact that the frequency bandwidth of the typical blood flow waveforms can vary drastically over the cardiac cycle.